Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.8K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.8K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.9K
Cluster Sampling Method01:20

Cluster Sampling Method

12.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Epithelial plasticity shapes intratumoral heterogeneity and cell lineages in early-stage lung cancer.

Science advances·2026
Same author

A novel stepwise and continuous high-volume irrigation drainage technique for managing cervical anastomotic leaks post-esophagectomy.

Journal of thoracic disease·2025
Same author

Takotsubo Cardiomyopathy After Orthotopic Liver Transplantation: A Case Series.

Transplantation proceedings·2025
Same author

Study on the change rule of airflow temperature field in ultra-deep mining shaft.

Scientific reports·2025
Same author

Incidental gall bladder cancer in the laparoscopic treatment and magnetic resonance imaging era: A single institution experience.

Journal of minimal access surgery·2023
Same author

Safety and efficacy of pulse-induced contour cardiac output monitoring in elderly patients with coronary artery disease and severe heart failure at coronary care units.

Frontiers in cardiovascular medicine·2022
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

Related Experiment Video

Updated: Sep 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm.

Zejun Li1,2, Li Ang1, Wei Shi2

  • 1School of Computer and Information Science, Hunan Institute of Technology, Hengyang 412002, China.

Computational and Mathematical Methods in Medicine
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm, fuzzy clustering and binary particle swarm optimization (FCBPSO), for selecting informative single-nucleotide polymorphisms (SNPs). FCBPSO improves disease prediction accuracy and reduces computation time compared to existing methods.

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Cryo-EM and Single-Particle Analysis with Scipion
09:06

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

3.9K

Related Experiment Videos

Last Updated: Sep 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Cryo-EM and Single-Particle Analysis with Scipion
09:06

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

3.9K

Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Biotechnology

Background:

  • Single-nucleotide polymorphisms (SNPs) are key genetic markers linked to diseases.
  • Current SNP genotyping methods have limitations in prediction accuracy and efficiency.
  • Identifying informative SNPs is crucial for accurate disease association studies.

Purpose of the Study:

  • To evaluate the impact of a novel fuzzy clustering and binary particle swarm optimization (FCBPSO) algorithm on informative SNP selection.
  • To enhance the accuracy and reduce the running time of SNP selection for disease association.
  • To compare FCBPSO with traditional methods like NMC, SPSO, and MCMR.

Main Methods:

  • Applied fuzzy clustering to identify equivalence relations and candidate tag SNPs, reducing redundancy.
  • Optimized the FCBPSO algorithm for final informative tag SNP set selection.
  • Compared prediction performance and running time of FCBPSO against NMC, SPSO, and MCMR.

Main Results:

  • FCBPSO demonstrated consistently higher prediction accuracy, especially with an increasing number of tag SNPs.
  • FCBPSO exhibited a lower running time compared to MCMR across all tested scenarios.
  • The algorithm effectively reduced the complexity of the optimization problem and simplified model training.

Conclusions:

  • FCBPSO offers a superior approach for informative SNP selection, balancing high prediction accuracy with computational efficiency.
  • The developed algorithm simplifies the prediction model training process.
  • FCBPSO represents a significant advancement over traditional methods for SNP-based disease association studies.